Coverage Optimization for UAV-Aided Internet of Things with Partial Channel Knowledge

Wang, Xuanxuan; Feng, Wei; Chen, Yunfei; Ge, Ning

doi:10.1007/s41650-018-0039-9

Cited by 11 publications

(8 citation statements)

References 36 publications

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“…The eavesdropper locates with a safety distance 100m away from the scheduled legitimate user to hide himself, and moves following the trajectory of the UAV swarm for better eavesdropping. Unless otherwise specified, the system parameters are set as follows: the number of antennas for the legitimate users N B = 4, the number of antennas for the eavesdropper N E = 2, the transmission duration τ = 4s, f = 2.4GHz [4], c = 3 × 10 8 m/s, a = 5.0188, b = 0.3511 [36] and the noise covariance δ 2 = −107dBm [41]. The threshold presented in Algorithm 1 is fixed as ǫ = 10 −3 .…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

“…where [x] + = max(0, x). 4 Different from the existing literatures [37], [38], AN in this work is designed by using only the large-scale CSI instead of the instantaneous legitimate CSI.…”

Section: B Problem Formulationmentioning

confidence: 99%

“…In recent years, unmanned aerial vehicles (UAVs) have attracted great interest in wireless communications [1]- [4]. Due to their mobility and elevated position, they can provide agile communications [5].…”

Section: Introductionmentioning

confidence: 99%

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UAV Swarm-Enabled Aerial CoMP: A Physical Layer Security Perspective

et al. 2019

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Unlike aerial base station enabled by a single unmanned aerial vehicle (UAV), aerial coordinated multiple points (CoMP) can be enabled by a UAV swarm. In this case, the management of multiple UAVs is important. This paper considers the power allocation strategy for a UAV swarm-enabled aerial network, to enhance the physical layer security of the downlink transmission, where an eavesdropper moves following the trajectory of the swarm for better eavesdropping. Unlike existing works, we use only the large-scale channel state information (CSI) and maximize the ergodic secrecy rate (ESR) in a wholetrajectory-oriented manner. The overall energy constraint on each UAV is considered. The non-convexity of the formulated problem is solved by using max-min optimization with iteration. Both the transmission power of desired signals and artificial noise (AN) are derived iteratively. Simulation results are presented to validate the effectiveness of our proposed power allocation algorithm and to show the advantage of aerial CoMP by using only the large-scale CSI. ). requirements for communications between multiple UAVs and 5G [11]- [15].One of the serious concerns in UAV swarm-enabled aerial networks is how to guarantee the privacy and secrecy of the system. Due to the broadcast nature and inherent randomness of wireless channels, UAV swarm-enabled communication networks are particularly vulnerable to various security threats, such as information eavesdropping, information leakage, data modification and so on. In addition, to facilitate the secure transmission, the UAV swarm often places itself near the legitimate users, which is beneficial to eavesdropping, especially when the eavesdropper moves close to the legitimate users. A. Related WorkTo achieve perfect security, the conventional encryption schemes are typically implemented at the upper layer using cryptographic methods. However, this is often achieved at the cost of high computational complexity [16].Unlike the traditional cryptographic methods, physical layer security (PLS), using the information-theoretic and signal processing approaches, has been widely investigated in the UAV-enabled wireless networks [17]-[20]. They enhance the coverage and security of the wireless systems by exploiting physical characteristics of the wireless channel. Specifically, by adaptively adjusting the UAVs' location, they could overcome the propagation constraints in the cellular systems, and provide new possibilities or opportunities for security enhancement. The authors in [17] utilized UAV as a mobile relay, and maximized the secrecy rate of the system with transmit optimization in a four-node. In [18], the authors investigated UAV-enabled secure communication systems where a mobile UAV sent confidential messages to multiple ground users. By considering the imperfect information on the locations of the eavesdroppers, the authors in [19] investigated a UAV-ground communication system with multiple potential eavesdroppers on the ground. The authors in [20] considered UAV-assisted secure c...

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Section: Numerical Results and Discussionmentioning

confidence: 99%

“…where [x] + = max(0, x). 4 Different from the existing literatures [37], [38], AN in this work is designed by using only the large-scale CSI instead of the instantaneous legitimate CSI.…”

Section: B Problem Formulationmentioning

confidence: 99%

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UAV Swarm-Enabled Aerial CoMP: A Physical Layer Security Perspective

et al. 2019

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“…the authors from [24] defines a model for high-altitude fixed-wing UAV A2G channel communications between aerial base stations. Wang et al [25] suggests coverage optimization considering small-scale and large-scale fading channels. Pereira [26] analyzes packet combining techniques using SC-FDE in the terrestrial networks.…”

Section: A Related Workmentioning

confidence: 99%

Increasing Reliability on UAV Fading Scenarios

et al. 2022

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Unmanned aerial vehicles (UAVs) are the next technology to be incorporated into a telecommunications network to improve command and control on a large scale in both lines of sight (LOS) and non-line of sight (NLOS) conditions. However, there is still room for improvement in terms of reliability. This paper investigates Constant Packet Combining (CPC) and Adaptive Packet Combining (APC) techniques applied to Unmanned Aerial Vehicle (UAV) communication in the presence of largescale fading, where the channels are subject to sudden degradation for long periods due to obstructions. We use Single Carrier (SC) Frequency Domain Equalization (FDE) combined with the Iterative Block Decision-Feedback Equalizer (IB-DFE) to handle command and control messages map for UAV use cases. We present closed-form equations for the equalization design and for the perfornance parameters such as Bit Error Rate (BER), the Packet Error Rate (PER), the throughput, the retransmissions amount, the goodput (the transmission rate without the retransmissions quantity), and the outage probability. Then, we analyze the system performance using correlated, independent, and equal channels. There is a trade-off between the overall available power, throughput, and reliability , for instance, more retransmissions result in higher reliability and power consumption and lower goodputs (effective data rates). CPC validates the transmission system and confirms the improvement in BER and PER parameters without energy efficiency optimization. The APC is appealing because it can reduce the number of retransmissions for all channels used with the advantage of meeting energy efficiency requirements by adapting the overall power to the scenario experienced by the UAV.

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“…Next, UAVs have fully controllable mobility in the three-dimensional (3D) space. Although this introduces new challenges in mobility management for wireless networks, such a feature can also be exploited with proper UAV positioning/trajectory design for performance optimization (see, e.g., [1,[8][9][10][11][12][13][14][15] and the references therein). Furthermore, security is another key issue faced in UAV-enabled wireless networks.…”

Section: Introductionmentioning

confidence: 99%

Secrecy Transmission in Large-Scale UAV-Enabled Wireless Networks

Yao

2019

IEEE Trans. Commun.

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This paper considers the secrecy transmission in a large-scale unmanned aerial vehicle (UAV)-enabled wireless network, in which a set of UAVs in the sky transmit confidential information to their respective legitimate receivers on the ground, in the presence of another set of randomly distributed suspicious ground eavesdroppers. We assume that the horizontal locations of legitimate receivers and eavesdroppers are distributed as two independent homogeneous Possion point processes (PPPs), and each of the UAVs is positioned exactly above its corresponding legitimate receiver for efficient secrecy communication. Furthermore, we consider an elevation-angledependent line-of-sight (LoS)/non-LoS (NLoS) path-loss model for air-to-ground (A2G) wireless channels and employ the wiretap code for secrecy transmission. Under such setups, we first characterize the secrecy communication performance (in terms of the connection probability, secrecy outage probability, and secrecy transmission capacity) in mathematically tractable forms, and accordingly optimize the system configurations (i.e., the wiretap code rates and UAV positioning altitude) to maximize the secrecy transmission capacity, subject to a maximum secrecy outage probability constraint. Next, we propose to use the secrecy guard zone technique for further secrecy protection, and analyze the correspondingly achieved secrecy communication performance. Finally, we present numerical results to validate the theoretical analysis. It is shown that the employment of secrecy guard zone significantly improves the secrecy transmission capacity of this network, and the desirable guard zone radius generally decreases monotonically as the UAVs' and/or the eavesdroppers' densities increase.Index Terms-UAV communications, physical layer security, homogeneous Poisson point process (PPP), secrecy transmission capacity, secrecy guard zone.

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Coverage Optimization for UAV-Aided Internet of Things with Partial Channel Knowledge

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 11 publications

References 36 publications

UAV Swarm-Enabled Aerial CoMP: A Physical Layer Security Perspective

UAV Swarm-Enabled Aerial CoMP: A Physical Layer Security Perspective

Increasing Reliability on UAV Fading Scenarios

Secrecy Transmission in Large-Scale UAV-Enabled Wireless Networks

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